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The filtered monotonic polynomial (FMP) model is a semi-parametric item response model that allows flexible response function shapes but also includes traditional item response models as special cases. The flexmet package for R facilitates the routine use of the FMP model in real data analysis and simulation studies. This tutorial provides several code examples illustrating how the flexmet package may be used to simulate FMP model parameters and data (both for dichotomous and polytomously scored items), estimate FMP model parameters, transform traditional item response models to different metrics, and more. This tutorial serves as both an introduction to the unique features of the FMP model and as a practical guide to its implementation in R via the flexmet package.

Statistical analyses are an essential part of regulatory toxicological evaluations. While projects would be ideally monitored by both toxicologists and statisticians, this is often not possible in practice. Hence, toxicologists should be trained in some common statistical approaches but also need a tool for statistical evaluations. Due to transparency needed in regulatory processes and standard tests that can be evaluated with template approaches, the freely available open-source statistical software R may be suitable. R is a well-established software in the statistical community. The principal input method is via software code, which is both benefit and weakness of the tool. It is increasingly used by regulating authorities globally and can be easily extended by software packages, e.g., for new statistical functions and features. This manuscript outlines how R can be used in regulatory toxicology, allowing toxicologists to perform all regulatory required data evaluations in a single software solution. Practical applications are shown in case studies on simulated and experimental data. The examples cover a) Dunnett testing of treatment groups against a common control and in relation to a biological relevance threshold, assessing the test’s assumptions and plotting the results; b) dose-response analysis and benchmark dose derivation for chronic kidney inflammation as a function of Pyridine; and c) graphical/exploratory data analysis of previously published developmental neurotoxicity data for Chlorpyrifos.

Purpose: Video has become a popular means for delivering \"how to\" information about a wide variety of software tasks. With video rapidly becoming a major instructional method, the question arises of their effectiveness for software training. This paper provides a set of eight guidelines for the construction of instructional videos for software training. Method: The guidelines present a concise view on how to design an instructional video for software training. They are based on a considerable body of research on how people process visual and verbal information and how to support these processes. Each guideline is described, illustrated, and supported with research findings from various disciplines. Results: The guidelines were tested in three consecutive empirical studies. In these studies a set of instructional videos for Word's formatting options were designed. The effects of the video instructions were compared with a paper tutorial (Van der Meij & Van der Meij, in preparation). We found that the video instructions yielded more favorable appraisals for motivation, higher skills proficiency immediately after training, and better skills retention after a one-week delay. Conclusions: The guidelines offer patterns that could further advance the theory and practice of the design of instructional videos for software training. A limitation of the study is that we concentrated on instructional video that serve a tutorial function. For video that function as a reference guide not all the guidelines are equally important, and also some new guidelines may be called for.

Purpose: Screenshots are an important means of visualization in software documentation. One question technical communicators need to address when dealing with screenshots is whether visual signaling elements, such as arrows or frames, should be added in order to highlight relevant information. This article reports the results of an experimental study that examined whether signaling elements successfully guide visual attention of readers to relevant screenshot information as intended. A second goal was to find out whether visual signaling has a positive impact on how accurate and fast users execute the tasks which the screenshots support. Method: Two versions of a software tutorial were constructed that included screenshots with or without signaling elements. Participants' eye movements were recorded while they studied the tutorial and executed the tasks described therein. In addition to eye movement measures, accuracy of task execution and time to complete the tasks were determined as measures of overall success on the tasks. Results: Participants working with tutorials that used visual signaling executed more tasks correctly. No differences were found regarding the time needed to complete the tasks. Analysis of the eye tracking data showed that participants fixated relevant screenshot areas longer and more often if highlighted by signaling elements. Conclusions: The results provide evidence that adding signaling elements to screenshots is an effective means to guide the visual attention of users. As predicted by the Cognitive Theory of Multimedia Learning, visual signaling does not simply increase interest in pictures but helps users to select relevant information.

This contribution presents the results of the â??Speech technology integrated learning modules for Intercultural Dialogue

Background: The constant growth in the body of knowledge in medicine requires pathologists and pathology trainees to engage in continuing education. Providing them with equitable access to efficient and effective forms of education in pathology (especially in remote and rural settings) is important, but challenging. Methods: We developed three pilot cytopathology virtual microscopy adaptive tutorials (VMATs) to explore a novel adaptive E-learning platform (AeLP) which can incorporate whole slide images for pathology education. We collected user feedback to further develop this educational material and to subsequently deploy randomized trials in both pathology specialist trainee and also medical student cohorts. Cytopathology whole slide images were first acquired then novel VMATs teaching cytopathology were created using the AeLP, an intelligent tutoring system developed by Smart Sparrow. The pilot was run for Australian pathologists and trainees through the education section of Royal College of Pathologists of Australasia website over a period of 9 months. Feedback on the usability, impact on learning and any technical issues was obtained using 5-point Likert scale items and open-ended feedback in online questionnaires. Results: A total of 181 pathologists and pathology trainees anonymously attempted the three adaptive tutorials, a smaller proportion of whom went on to provide feedback at the end of each tutorial. VMATs were perceived as effective and efficient E-learning tools for pathology education. User feedback was positive. There were no significant technical issues. Conclusion: During this pilot, the user feedback on the educational content and interface and the lack of technical issues were helpful. Large scale trials of similar online cytopathology adaptive tutorials were planned for the future.

Several software tools are available that facilitate the use of the NONMEM software and extend its functionality. This tutorial shows how three commonly used and freely available tools, Pirana, PsN, and Xpose, form a tightly integrated workbench for modeling and simulation with NONMEM. During the tutorial, we provide some guidance on what diagnostics we consider most useful in pharmacokinetic model development and how to construct them using these tools. CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e50; doi:10.1038/psp.2013.24; advance online publication 26 June 2013

Despite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML) models for drug development, effectively interpreting their predictions remains a challenge, which limits their impact on clinical decisions. We address this issue by providing a practical guide to SHapley Additive exPlanations (SHAP), a popular feature‐based interpretability method, which can be seamlessly integrated into supervised ML models to gain a deeper understanding of their predictions, thereby enhancing their transparency and trustworthiness. This tutorial focuses on the application of SHAP analysis to standard ML black‐box models for regression and classification problems. We provide an overview of various visualization plots and their interpretation, available software for implementing SHAP, and highlight best practices, as well as special considerations, when dealing with binary endpoints and time‐series models. To enhance the reader's understanding for the method, we also apply it to inherently explainable regression models. Finally, we discuss the limitations and ongoing advancements aimed at tackling the current drawbacks of the method.

In this tutorial, the various components of NONMEM will be described, and the basic steps of setting up NONMEM control stream files and data files will be demonstrated. Some basic concepts of nonlinear mixed effects modeling will be discussed, along with simple examples demonstrating how to use NONMEM to perform population analysis of clinical data.